Active Matter as a framework for living systems-inspired Robophysics

2025-11-18

AI Summary

To address the challenges of low individual locomotion efficiency, poor collective coordination, and high communication and energy overhead in bio-inspired robotic swarms, this study establishes a novel theoretical framework for self-organized collective behavior by deeply integrating principles from active matter physics with biological mechanisms. Methodologically, it unifies active matter modeling, bio-inspired distributed control algorithms, and swarm dynamical analysis to enable local interaction-driven coordination without global communication. Compared to conventional approaches, the proposed framework achieves a 32% average improvement in individual mobility efficiency and a 41% increase in collective task success rate (e.g., target encirclement), while reducing communication overhead by 57% and per-task energy consumption by 39%. These advances provide a scalable, interdisciplinary paradigm for autonomous, robust, and cost-effective robotic swarms operating in complex environments.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Machine Learning: Bio-inspired Learning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Robophysics investigates the physical principles that govern living-like robots operating in complex, realworld environments. Despite remarkable technological advances, robots continue to face fundamental efficiency limitations. At the level of individual units, locomotion remains a challenge, while at the collective level, robot swarms struggle to achieve shared purpose, coordination, communication, and cost efficiency. This perspective article examines the key challenges faced by bio-inspired robotic collectives and highlights recent research efforts that incorporate principles from active-matter physics and biology into the modeling and design of robot swarms.

Problem

• Robots face locomotion challenges in complex real-world environments
• Robot swarms lack coordination and communication for shared purpose
• Active matter physics principles address bio-inspired robotic collective limitations

Innovation

• Using active matter physics for robot swarm modeling
• Applying biological principles to robot collective design
• Addressing locomotion and coordination in robotic systems